The analogy in **MongoDB** would be a "collection".

In the code, a `Bucket` represents the main entrypoint of communication with the database.

This utility function will:

* Connect to a **Couchbase** cluster (that might be a single machine).
    * Set defaults for timeouts.
* Authenticate in the cluster.
* Get a `Bucket` instance.
    * Set defaults for timeouts.

    And be compatible with all of them at the same time.

Create a utility function to hash a password coming from the user.

And another utility to verify if a received password matches the hash stored.

And another one to authenticate and return a user.

=== "Python 3.10+"

Because it's a Python exception, you don't `return` it, you `raise` it.

!!! tip
    You could adopt ideas from the section about SQLAlchemy ORM ([SQL (Relational) Databases](../tutorial/sql-databases.md){.internal-link target=_blank}), like using utility functions to perform operations in the database, independent of your **FastAPI** code.

        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="25"
    {!> ../../../docs_src/security/tutorial002.py!}
    ```

## Get the user

`get_current_user` will use a (fake) utility function we created, that takes a token as a `str` and returns our Pydantic `User` model:

=== "Python 3.10+"

    ```Python hl_lines="19-22  26-27"
    {!> ../../../docs_src/security/tutorial002_an_py310.py!}

Import `models` (the SQLAlchemy models) and `schemas` (the Pydantic *models* / schemas).

Create utility functions to:

* Read a single user by ID and by email.
* Read multiple users.
* Read multiple items.

```Python hl_lines="1  3  6-7  10-11  14-15  27-28"
{!../../../docs_src/sql_databases/sql_app/crud.py!}

That means that any value read in Python from an environment variable will be a `str`, and any conversion to a different type or validation has to be done in code.

## Pydantic `Settings`

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.

La hiérarchie est la suivante :

* **Uvicorn** : un serveur ASGI
    * **Starlette** : (utilise Uvicorn) un micro-framework web
        * **FastAPI**: (utilise Starlette) un micro-framework pour API disposant de fonctionnalités additionnelles pour la création d'API, avec la validation des données, etc.

* **Uvicorn** :

Dans les versions précédentes de Python, vous auriez utilisé des *threads* ou <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>.  Mais le code aurait été bien plus difficile à comprendre, débugger, et concevoir.

* Utilize os mesmos exemplos Python e somente traduza o texto na documentação. Você não tem que alterar nada no código para que funcione.

* Utilize as mesmas imagens, nomes de arquivo e links. Você não tem que alterar nada disso para que funcione.